A Comparative Study of Machine Learning Models Predicting Energetics of Interacting Defects

arXiv:2403.13243v1 Announce Type: cross
Abstract: Interacting defect systems are ubiquitous in materials under realistic scenarios, yet gaining an atomic-level understanding of these systems from a computational perspective is challenging - it often demands substantial resources due to the necessity of employing supercell calculations. While machine learning techniques have shown potential in accelerating materials simulations, their application to systems involving interacting defects remains relatively rare. In this work, we present a comparative study of three different methods to predict the free …

abstract arxiv computational cond-mat.mtrl-sci cs.lg defects machine machine learning machine learning models machine learning techniques materials perspective physics.comp-ph resources study systems type understanding